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Multilevel Parallelization of Unsupervised Learning Algorithms in Pattern Recognition on a Roadrunner Architecture

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Intelligent Distributed Computing V

Part of the book series: Studies in Computational Intelligence ((SCI,volume 382))

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Abstract

The aim of the paper is to present a solution to the NP hard problem of determining a partition of equivalence classes for a finite set of patterns. The system must learn the classification of the weighted patterns without any information about the number of pattern classes, based on a finite set of patterns in a metric pattern space. Because a metric is not suitable in all the cases to build an equivalence relation, an ultrametric is generated from indexed hierarchies. The contributions presented in this paper consists in the proposal of multilevel parallel algorithms for bottom-up hierarchical clustering, and hence for generating ultra-metrics based on the metrics provided by the user. The algorithms were synthesized and optimized for clusters having the Roadrunner architecture (the first supercomputer that breaks 1PFlops barrier [1]).

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References

  1. Barker, K.J., Davis, K., Hoisie, A., Kerbyson, D.J., Lang, M., Pakin, S., Sancho, J.C.: Entering the petaflop era: The architecture and performance of Roadrunner. High Performance Computing, Networking, Storage and Analysis, 1–11 (2008), doi:10.1109/SC.2008.5217926

    Google Scholar 

  2. Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 551–556. ACM, New York (2004), doi:10.1145/1014052.1014118

    Chapter  Google Scholar 

  3. Milligan, G.W., Isaac, P.D.: The validation of four ultrametric clustering algorithms. Pattern Recognition 12(2), 41–50 (1980); doi:10.1016/0031-3203(80)90001-1, ISSN 0031-3203

    Google Scholar 

  4. Celeux, G., Diday, E., et al.: Classification automatiqur de donees. In: Environement statistique et informatique, Dunod Informatique. Dunod Informatique, Bordas Paris (1989)

    Google Scholar 

  5. GuoYan, H., DongMei, Z., Jiadong, R., ChangZhen, H.: A Hierarchical Clustering Algorithm Based on K-Means with Constraints. In: Innovative Computing, Information and Control (ICICIC), pp. 1479–1482 (2009), doi:10.1109/ICICIC.2009.18

    Google Scholar 

  6. Jun, Q., Qingshan, J., Fangfei, W., Zhiling, H.: A Hierarchical Clustering Based on Overlap Similarity Measure. Software Engineering, Artificial Intelligence, Networking, and Parallel, Distributed Computing 3, 905–910 (2007) ;doi:10.1109/SNPD.2007.502

    Google Scholar 

  7. Sandeep, K.: Practical Computing on the Cell Broadband Engine. Springer, Heidelberg (2009); ISBN: 978-1-4419-0307-5

    Google Scholar 

  8. Vogt, J.S., Land, R., Boettiger, H., Krnjajic, Z., Baier, H.: IBM BladeCenter QS22: Design, performance, and utilization in hybrid computing systems. IBM Journal of Research and Development 53(5) (2009); doi:10.1147/JRD.2009.5429069

    Google Scholar 

  9. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2007)

    Google Scholar 

  10. Guochun, S., Kindratenko, V., Pratas, F., Trancoso, P., Gschwind, M.: Application Acceleration with the Cell Broadband Engine. Computing in Science & Engineering 12(1), 76–81 (2010); doi:10.1109/MCSE.2010.4

    Article  Google Scholar 

  11. Dursun, H., Barker, K.J., Kerbyson, D.J., Pakin, S.: Application profiling on Cell-based clusters. Parallel & Distributed Processing, 1–8 (2009); doi:10.1109/IPDPS.2009.5161092

    Google Scholar 

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Authors and Affiliations

  1. University “Stefan cel Mare” of Suceava, str. Universitatii no.13, RO-720229, Suceava, Romania

    Stefan-Gheorghe Pentiuc & Ioan Ungurean

Authors
  1. Stefan-Gheorghe Pentiuc
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  2. Ioan Ungurean
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Editor information

Editors and Affiliations

  1. Faculty of Technology,Policy andManagement, Intelligent Interactive Dynamic Systems, Section Systems Engineering, Delft University of Technology, Jaffalaan 5, 2628BX, Delft, The Netherlands

    F. M. T. Brazier

  2. D-CIS lab, P.O. Box 90, 2600 AB, Delft, The Netherlands

    Kees Nieuwenhuis  & Gregor Pavlin  & 

  3. Faculty of Technology,Policy andManagement, Section Systems Engineering, Delft University of Technology, Jaffalaan 5, 2628BX, Delft, The Netherlands

    Martijn Warnier

  4. Faculty of Automatics, Computers and Electronics, Software Engineering Department, University of Craiova, Bvd. Decebal Nr. 107, 200440, Craiova, Romania

    Costin Badica

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© 2011 Springer-Verlag Berlin Heidelberg

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Pentiuc, SG., Ungurean, I. (2011). Multilevel Parallelization of Unsupervised Learning Algorithms in Pattern Recognition on a Roadrunner Architecture. In: Brazier, F.M.T., Nieuwenhuis, K., Pavlin, G., Warnier, M., Badica, C. (eds) Intelligent Distributed Computing V. Studies in Computational Intelligence, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24013-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-24013-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24012-6

  • Online ISBN: 978-3-642-24013-3

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